python合法标识符int32_Python torch.int32方法代码示例

本文整理汇总了Python中torch.int32方法的典型用法代码示例。如果您正苦于以下问题：Python torch.int32方法的具体用法？Python torch.int32怎么用？Python torch.int32使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块torch的用法示例。

在下文中一共展示了torch.int32方法的24个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: normalize_wav

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# 需要导入模块: import torch [as 别名]

# 或者: from torch import int32 [as 别名]

def normalize_wav(tensor: torch.Tensor) -> torch.Tensor:

if tensor.dtype == torch.float32:

pass

elif tensor.dtype == torch.int32:

tensor = tensor.to(torch.float32)

tensor[tensor > 0] /= 2147483647.

tensor[tensor < 0] /= 2147483648.

elif tensor.dtype == torch.int16:

tensor = tensor.to(torch.float32)

tensor[tensor > 0] /= 32767.

tensor[tensor < 0] /= 32768.

elif tensor.dtype == torch.uint8:

tensor = tensor.to(torch.float32) - 128

tensor[tensor > 0] /= 127.

tensor[tensor < 0] /= 128.

return tensor

开发者ID:pytorch，项目名称:audio，代码行数:18，

示例2: generate_iters_indices

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# 需要导入模块: import torch [as 别名]

# 或者: from torch import int32 [as 别名]

def generate_iters_indices(self, num_of_iters):

from_iter = len(self.iter_indices_per_iteration)

for iter_num in range(from_iter, from_iter+num_of_iters):

# Get random number of samples per task (according to iteration distribution)

tsks = Categorical(probs=self.tasks_probs_over_iterations[iter_num]).sample(torch.Size([self.samples_in_batch]))

# Generate samples indices for iter_num

iter_indices = torch.zeros(0, dtype=torch.int32)

for task_idx in range(self.num_of_tasks):

if self.tasks_probs_over_iterations[iter_num][task_idx] > 0:

num_samples_from_task = (tsks == task_idx).sum().item()

self.samples_distribution_over_time[task_idx].append(num_samples_from_task)

# Randomize indices for each task (to allow creation of random task batch)

tasks_inner_permute = np.random.permutation(len(self.tasks_samples_indices[task_idx]))

rand_indices_of_task = tasks_inner_permute[:num_samples_from_task]

iter_indices = torch.cat([iter_indices, self.tasks_samples_indices[task_idx][rand_indices_of_task]])

else:

self.samples_distribution_over_time[task_idx].append(0)

self.iter_indices_per_iteration.append(iter_indices.tolist())

开发者ID:igolan，项目名称:bgd，代码行数:21，

示例3: compute_logits

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# 需要导入模块: import torch [as 别名]

# 或者: from torch import int32 [as 别名]

def compute_logits(self, token_ids: torch.Tensor) -> torch.Tensor:

"""

Implements a language model, where each output is conditional on the current

input and inputs processed so far.

Args:

inputs: int32 tensor of shape [B, T], storing integer IDs of tokens.

Returns:

torch.float32 tensor of shape [B, T, V], storing the distribution over output symbols

for each timestep for each batch element.

"""

# TODO 5# 1) Embed tokens

# TODO 5# 2) Run RNN on embedded tokens

# TODO 5# 3) Project RNN outputs onto the vocabulary to obtain logits.

return rnn_output_logits

开发者ID:microsoft，项目名称:machine-learning-for-programming-samples，代码行数:18，

示例4: test_one_hot

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# 需要导入模块: import torch [as 别名]

# 或者: from torch import int32 [as 别名]

def test_one_hot(self):

"""

Tests a torch one hot function.

"""

if get_backend() == "pytorch":

# Flat action array.

inputs = torch.tensor([0, 1], dtype=torch.int32)

one_hot = pytorch_one_hot(inputs, depth=2)

expected = torch.tensor([[1., 0.], [0., 1.]])

recursive_assert_almost_equal(one_hot, expected)

# Container space.

inputs = torch.tensor([[0, 3, 2],[1, 2, 0]], dtype=torch.int32)

one_hot = pytorch_one_hot(inputs, depth=4)

expected = torch.tensor([[[1, 0, 0, 0],[0, 0, 0, 1],[0, 0, 1, 0]],[[0, 1, 0, 0],[0, 0, 1, 0],[1, 0, 0, 0,]]],

dtype=torch.int32)

recursive_assert_almost_equal(one_hot, expected)

开发者ID:rlgraph，项目名称:rlgraph，代码行数:21，

示例5: _graph_fn_call

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# 需要导入模块: import torch [as 别名]

# 或者: from torch import int32 [as 别名]

def _graph_fn_call(self, inputs):

if self.backend == "python" or get_backend() == "python":

if isinstance(inputs, list):

inputs = np.asarray(inputs)

return inputs.astype(dtype=util.convert_dtype(self.to_dtype, to="np"))

elif get_backend() == "pytorch":

torch_dtype = util.convert_dtype(self.to_dtype, to="pytorch")

if torch_dtype == torch.float or torch.float32:

return inputs.float()

elif torch_dtype == torch.int or torch.int32:

return inputs.int()

elif torch_dtype == torc

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